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Journals / International Journal of Assessment Tools in Education / 2018 / Cilt: 5 - Sayı: 3

Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Ergün Akgün
Metin DEMİR
Pages
491–509
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The fact that the artificial neural network performance in this study was R=0.84 for the Science and Technology Education I course, and R=0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it was aimed to predict elementary education teacher candidates’ achievements in “Science and Technology Education I and II” courses by using artificial neural networks.
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Key Findings & Data:In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected.
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Conclusion & Impact:The fact that the artificial neural network performance in this study was R=0.84 for the Science and Technology Education I course, and R=0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Elementary Education · Science and Technology Teaching · Data Mining

Abstract

In this study, it was aimed to predict elementary education teacher candidates’ achievements in “Science and Technology Education I and II” courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected. The fact that the artificial neural network performance in this study was R=0.84 for the Science and Technology Education I course, and R=0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

Özet

In this study, it was aimed to predict elementary education teacher candidates’ achievements in “Science and Technology Education I and II” courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected. The fact that the artificial neural network performance in this study was R=0.84 for the Science and Technology Education I course, and R=0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

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Cite This Article

Akgün, E., DEMİR, M. (2022). Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks. International Journal of Assessment Tools in Education, Cilt: 5 - Sayı: 3, 491–509. https://ajindex.com/articles/540230

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